Assessing Responsible Innovation Training

Bernd Carsten Stahl; Christine Aicardi; Laurence Brooks; Peter J. Craigon; Mayen Cunden; Saheli Datta Burton; Martin De Heaver; Stevienna De Saille; Serena Dolby; Liz Dowthwaite; Damian Eke; Stephen Hugues; Paul Keene; Vivienne Kuh; Virginia Portillo; Danielle Shanley; Melanie Smallman; Michael Smith; Jack Stilgoe; Inga Ulnicane; Christian Wagner; Helena Webb

doi:10.1016/j.jrt.2023.100063

Assessing Responsible Innovation Training

Bernd Carsten Stahl^*, Christine Aicardi, Laurence Brooks, Peter J. Craigon, Mayen Cunden, Saheli Datta Burton, Martin De Heaver, Stevienna De Saille, Serena Dolby, Liz Dowthwaite, Damian Eke, Stephen Hugues, Paul Keene, Vivienne Kuh, Virginia Portillo, Danielle Shanley, Melanie Smallman, Michael Smith, Jack Stilgoe, Inga UlnicaneChristian Wagner, Helena Webb

^*Corresponding author for this work

Research output: Contribution to journal › Editorial

4 Citations (Scopus)

Abstract

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI.

Original language	English
Article number	100063
Journal	Journal of Responsible Technology
Volume	16
DOIs	https://doi.org/10.1016/j.jrt.2023.100063
Publication status	Published - 10 Jun 2023

Keywords

responsible innovation
doctoral training
education
assessment

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10.1016/j.jrt.2023.100063Licence: CC BY-NC-ND

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Stahl, B. C., Aicardi, C., Brooks, L., Craigon, P. J., Cunden, M., Datta Burton, S., De Heaver, M., De Saille, S., Dolby, S., Dowthwaite, L., Eke, D., Hugues, S., Keene, P., Kuh, V., Portillo, V., Shanley, D., Smallman, M., Smith, M., Stilgoe, J., ... Webb, H. (2023). Assessing Responsible Innovation Training. Journal of Responsible Technology, 16, Article 100063. https://doi.org/10.1016/j.jrt.2023.100063

@article{452beb5e0c6d41759a5a664a9522e8be,

title = "Assessing Responsible Innovation Training",

abstract = "There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI.",

keywords = "responsible innovation, doctoral training, education, assessment",

author = "Stahl, {Bernd Carsten} and Christine Aicardi and Laurence Brooks and Craigon, {Peter J.} and Mayen Cunden and {Datta Burton}, Saheli and {De Heaver}, Martin and {De Saille}, Stevienna and Serena Dolby and Liz Dowthwaite and Damian Eke and Stephen Hugues and Paul Keene and Vivienne Kuh and Virginia Portillo and Danielle Shanley and Melanie Smallman and Michael Smith and Jack Stilgoe and Inga Ulnicane and Christian Wagner and Helena Webb",

note = "Funding Information: The Horizon Centre for Doctoral Training is supported by: UKRI Grant No. EP/L015463/1 (EPSRC Horizon CDT in My Life in Data), UKRI Grant No. EP/S023305/1 (EPSRC Horizon CDT in Creating Our Lives in Data). This work was supported by the UKRI grants: EP/T022493/1 (Horizon: Trusted Data-Driven Products) and EP/V00784X/1 (TAS Hub). Christine Aicardi's work is supported by UK Research and Innovation [grant number EP/S023356/1 UKRI CDT in Safe and Trusted Artificial Intelligence] and by the Engineering and Physical Sciences Research Council [grant number EP/S022104/1 EPSRC CDT in Smart Medical Imaging]. Vivienne Kuh's work is supported by the in-kind contribution of University of Bristol to the following EPSRC CDT grants: EP/S021728/1 CDT in Composites Science, Engineering and Manufacturing, EP/S021795/1 CDT in in Future Autonomous Robotic Systems (FARSCOPE-TU: Towards Ubiquity), EP/S023593/1 CDT in Aerosol Science, EP/S024107/1 CDT in Technology Enhanced Chemical Synthesis, EP/S022937 CDT in Interactive Artificial Intelligence, EP/S023275/1 CDT in Future Innovation in Non-Destructive evaluation (FIND), EP/S023607/1 CDT in Quantum Engineering EP/S023569/1 CDT in Computational Statistics and Data Science: COMPASS. The UCL RRI Hub is supported by the following: UKRI EPSRC Responsible AI for Long-term Trustworthy Autonomous Systems (RAILS): Integrating Responsible AI and Socio-legal Governance (EPSRC Grant Ref: EP/W011344/1); EPSRC & SFI CDT in Energy Resilience and the Built Environment with the University of Loughborough (EPSRC Grant Ref: EP/S021671/1); EPSRC & SFI Centre for Doctoral Training in Transformative Pharmaceutical Technologies (EPSRC Grant Ref: EP/S023054/1); EPSRC CDT in AI-enabled Healthcare Systems (EPSRC Grant Ref: EP/S021612/1); EPSRC CDT in Geometry and Number Theory at the Interface (known as {\textquoteleft}The London School of Geometry and Number Theory{\textquoteright}) (EPSRC Grant Ref: EP/S021590/1); EPSRC CDT in Bioprocess Engineering Leadership (EPSRC Grant Ref: EP/S021868/1); EPSRC CDT in BioDesign Engineering with the University of Manchester and Imperial College (EPSRC Grant Ref: EP/S022856/1); EPSRC CDT in Delivering Quantum Technologies (EPSRC Grant Ref: EP/S021582/1); EPSRC CDT in Cybersecurity (EPSRC Grant Ref: EP/S022503/1); EPSRC CDT in Intelligent, Integrated Imaging In Healthcare (EPSRC Grant Ref: EP/S021930/1); EPSRC CDT in Compound Semiconductor Manufacture with the University of Cardiff (EPSRC Grant Ref: EPS024441/1); EPSRC CDT in Foundational Artificial Intelligence (EPSRC Grant Ref: EP/S021566/1); EPSRC CDT in Connected Electronic and Photonic Systems with the University of Cambridge (EPSRC Grant Ref: EP/S022139/1). The work undertaken by Bernd Stahl, Inga Ulnicane, Damian Eke and Paul Keene has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Grant Agreement no. 945539 (HBP SGA3). We would like to thank Adrian Hazzard for his input on the Horizon CDT case study. Publisher Copyright: {\textcopyright} 2023",

year = "2023",

month = jun,

day = "10",

doi = "10.1016/j.jrt.2023.100063",

language = "English",

volume = "16",

journal = "Journal of Responsible Technology",

issn = "2666-6596",

publisher = "Elsevier",

}

Stahl, BC, Aicardi, C, Brooks, L, Craigon, PJ, Cunden, M, Datta Burton, S, De Heaver, M, De Saille, S, Dolby, S, Dowthwaite, L, Eke, D, Hugues, S, Keene, P, Kuh, V, Portillo, V, Shanley, D, Smallman, M, Smith, M, Stilgoe, J, Ulnicane, I, Wagner, C & Webb, H 2023, 'Assessing Responsible Innovation Training', Journal of Responsible Technology, vol. 16, 100063. https://doi.org/10.1016/j.jrt.2023.100063

TY - JOUR

T1 - Assessing Responsible Innovation Training

AU - Stahl, Bernd Carsten

AU - Aicardi, Christine

AU - Brooks, Laurence

AU - Craigon, Peter J.

AU - Cunden, Mayen

AU - Datta Burton, Saheli

AU - De Heaver, Martin

AU - De Saille, Stevienna

AU - Dolby, Serena

AU - Dowthwaite, Liz

AU - Eke, Damian

AU - Hugues, Stephen

AU - Keene, Paul

AU - Kuh, Vivienne

AU - Portillo, Virginia

AU - Shanley, Danielle

AU - Smallman, Melanie

AU - Smith, Michael

AU - Stilgoe, Jack

AU - Ulnicane, Inga

AU - Wagner, Christian

AU - Webb, Helena

N1 - Funding Information: The Horizon Centre for Doctoral Training is supported by: UKRI Grant No. EP/L015463/1 (EPSRC Horizon CDT in My Life in Data), UKRI Grant No. EP/S023305/1 (EPSRC Horizon CDT in Creating Our Lives in Data). This work was supported by the UKRI grants: EP/T022493/1 (Horizon: Trusted Data-Driven Products) and EP/V00784X/1 (TAS Hub). Christine Aicardi's work is supported by UK Research and Innovation [grant number EP/S023356/1 UKRI CDT in Safe and Trusted Artificial Intelligence] and by the Engineering and Physical Sciences Research Council [grant number EP/S022104/1 EPSRC CDT in Smart Medical Imaging]. Vivienne Kuh's work is supported by the in-kind contribution of University of Bristol to the following EPSRC CDT grants: EP/S021728/1 CDT in Composites Science, Engineering and Manufacturing, EP/S021795/1 CDT in in Future Autonomous Robotic Systems (FARSCOPE-TU: Towards Ubiquity), EP/S023593/1 CDT in Aerosol Science, EP/S024107/1 CDT in Technology Enhanced Chemical Synthesis, EP/S022937 CDT in Interactive Artificial Intelligence, EP/S023275/1 CDT in Future Innovation in Non-Destructive evaluation (FIND), EP/S023607/1 CDT in Quantum Engineering EP/S023569/1 CDT in Computational Statistics and Data Science: COMPASS. The UCL RRI Hub is supported by the following: UKRI EPSRC Responsible AI for Long-term Trustworthy Autonomous Systems (RAILS): Integrating Responsible AI and Socio-legal Governance (EPSRC Grant Ref: EP/W011344/1); EPSRC & SFI CDT in Energy Resilience and the Built Environment with the University of Loughborough (EPSRC Grant Ref: EP/S021671/1); EPSRC & SFI Centre for Doctoral Training in Transformative Pharmaceutical Technologies (EPSRC Grant Ref: EP/S023054/1); EPSRC CDT in AI-enabled Healthcare Systems (EPSRC Grant Ref: EP/S021612/1); EPSRC CDT in Geometry and Number Theory at the Interface (known as ‘The London School of Geometry and Number Theory’) (EPSRC Grant Ref: EP/S021590/1); EPSRC CDT in Bioprocess Engineering Leadership (EPSRC Grant Ref: EP/S021868/1); EPSRC CDT in BioDesign Engineering with the University of Manchester and Imperial College (EPSRC Grant Ref: EP/S022856/1); EPSRC CDT in Delivering Quantum Technologies (EPSRC Grant Ref: EP/S021582/1); EPSRC CDT in Cybersecurity (EPSRC Grant Ref: EP/S022503/1); EPSRC CDT in Intelligent, Integrated Imaging In Healthcare (EPSRC Grant Ref: EP/S021930/1); EPSRC CDT in Compound Semiconductor Manufacture with the University of Cardiff (EPSRC Grant Ref: EPS024441/1); EPSRC CDT in Foundational Artificial Intelligence (EPSRC Grant Ref: EP/S021566/1); EPSRC CDT in Connected Electronic and Photonic Systems with the University of Cambridge (EPSRC Grant Ref: EP/S022139/1). The work undertaken by Bernd Stahl, Inga Ulnicane, Damian Eke and Paul Keene has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Grant Agreement no. 945539 (HBP SGA3). We would like to thank Adrian Hazzard for his input on the Horizon CDT case study. Publisher Copyright: © 2023

PY - 2023/6/10

Y1 - 2023/6/10

N2 - There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI.

AB - There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI.

KW - responsible innovation

KW - doctoral training

KW - education

KW - assessment

UR - http://www.scopus.com/inward/record.url?scp=85169898738&partnerID=8YFLogxK

U2 - 10.1016/j.jrt.2023.100063

DO - 10.1016/j.jrt.2023.100063

M3 - Editorial

SN - 2666-6596

VL - 16

JO - Journal of Responsible Technology

JF - Journal of Responsible Technology

M1 - 100063

ER -

Assessing Responsible Innovation Training

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